Supplementary data are available at Bioinformatics online.

TMCO1 (transmembrane and coiled-coil domains 1) is an endoplasmic reticulum (ER) transmembrane protein that actively prevents Ca2+ stores from overfilling. To characterize its physiological function(s), we generated Tmco1−/− knockout (KO) mice. In addition to the main clinical features of human cerebrofaciothoracic (CFT) dysplasia spectrum, Tmco1−/− females manifest gradual loss of ovarian follicles, impaired ovarian follicle development, and subfertility with a phenotype analogous to the premature ovarian failure (POF) in women. In line with the role of TMCO1 as a Ca2+ load-activated Ca2+ channel, we have detected a supernormal Ca2+ signaling in Tmco1−/− granulosa cells (GCs). Interestingly, although spontaneous Ca2+ oscillation pattern was altered, ER Ca2+ stores of germinal vesicle (GV) stage oocytes and metaphase II (MII) arrested eggs were normal upon Tmco1 ablation. Combined with RNA-sequencing analysis, we also detected increased ER stress-mediated apoptosis and enhanced reactive oxygen species (ROS) level in Tmco1−/− GCs, indicating the dysfunctions of GCs upon TMCO1 deficiency. Taken together, these results reveal that TMCO1 is essential for ovarian follicle development and female fertility by maintaining ER Ca2+ homeostasis of GCs, disruption of which causes ER stress-mediated apoptosis and increased cellular ROS level in GCs and thus leads to impaired ovarian follicle development.

No abstract

Herein, pressure-induced phase transitions of RDX up to 50 GPa were systematically studied under different compression conditions. Precise phase transition points were obtained based on high-quality Raman spectra with small pressure intervals. This favors the correctness of the theoretical formula for detonation and the design of a precision weapon. The experimental results indicated that α-RDX immediately transformed to γ-RDX at 3.5 GPa due to hydrostatic conditions and possible interaction between the penetrating helium and RDX, with helium gas as the pressure-transmitting medium (PTM). Mapping of pressure distribution in samples demonstrates that the pressure gradient is generated in the chamber and independent of other PTMs. The gradient induced the first phase transition starts at 2.3 GPa and completed at 4.1 GPa. The larger pressure gradient promoted phase transition in advance under higher pressures. Experimental results supported that there existed two conformers of AAI and AAE for γ-RDX, as proposed by another group. δ-RDX was considered to only occur in a hydrostatic environment around 18 GPa using helium as the PTM. This study confirms that δ-RDX is independent of PTM and exists under non-hydrostatic conditions. Evidence for a new phase (ζ) was found at about 28 GPa. These 4 phases have also been verified via XRD under high pressures. In addition to this, another new phase (η) may exist above 38 GPa, and it needs to be further confirmed in the future. Moreover, all the phase transitions were reversible after the pressure was released, and original α-RDX was always obtained at ambient pressure.

Trinitrohexahydro-s-triazine (RDX) single-crystal particles crystallized in the β phase have been successfully grown by sublimation−recrystallization. Synchrotron X-ray diffraction results confirm that the recrystallized products are β-RDX single crystals. Compared to the traditional solution deposition methods, the sublimation method used in this work breaks through the mass limitations during the growth of β-RDX. The distribution of αand β-RDX on different substrates reveals that hydrophilic substrates are prone to grow β-RDX single crystals and that hydrophobic substrates prefer α-RDX single crystals. The sublimation rate dominates the growth of pure β-RDX. The mechanisms of the phase transition from β-RDX to α-RDX during the process of sublimation−recrystallization have been studied. The current results can promote the potential application of β-RDX in the propellant, explosives, and military fields.

Integration of distinct biological data types could provide a comprehensive view of biological processes or complex diseases. The combinations of molecules responsible for different phenotypes form multiple embedded (expression) subspaces, thus identifying the intrinsic data structure is challenging by regular integration methods. In this paper, we propose a novel framework of “Multi-view Subspace Clustering Analysis (MSCA),” which could measure the local similarities of samples in the same subspace and obtain the global consensus sample patterns (structures) for multiple data types, thereby comprehensively capturing the underlying heterogeneity of samples. Applied to various synthetic datasets, MSCA performs effectively to recognize the predefined sample patterns, and is robust to data noises. Given a real biological dataset, i.e., Cancer Cell Line Encyclopedia (CCLE) data, MSCA successfully identifies cell clusters of common aberrations across cancer types. A remarkable superiority over the state-of-the-art methods, such as iClusterPlus, SNF, and ANF, has also been demonstrated in our simulation and case studies.

The integration of multi-omics data makes it possible to understand complex biological organisms at the system level. Numerous integration approaches have been developed by assuming a common underlying data space. Due to the noise and heterogeneity of biological data, the performance of these approaches is greatly affected. In this work, we propose a novel deep neural network architecture, named Deep Latent Space Fusion (DLSF), which integrates the multi-omics data by learning consistent manifold in the sample latent space for disease subtypes identification. DLSF is built upon a cycle autoencoder with a shared self-expressive layer, which can naturally and adaptively merge nonlinear features at each omics level into one unified sample manifold and produce adaptive representation of heterogeneous samples at the multi-omics level. We have assessed DLSF on various biological and biomedical datasets to validate its effectiveness. DLSF can efficiently and accurately capture the intrinsic manifold of the sample structures or sample clusters compared with other state-of-the-art methods, and DLSF yielded more significant outcomes for biological significance, survival prognosis and clinical relevance in application of cancer study in The Cancer Genome Atlas. Notably, as a deep case study, we determined a new molecular subtype of kidney renal clear cell carcinoma that may benefit immunotherapy in the viewpoint of multi-omics, and we further found potential subtype-specific biomarkers from multiple omics data, which were validated by independent datasets. In addition, we applied DLSF to identify potential therapeutic agents of different molecular subtypes of chronic lymphocytic leukemia, demonstrating the scalability of DLSF in diverse omics data types and application scenarios.

Significantly increasing crop yield is a major and worldwide challenge for food supply and security. It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide. Yet, the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery. Here, we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group. We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method, i.e. , dynamic cross-tissue (DCT) network analysis. We used one of the candidate genes, OsSPL4 , whose function was previously unknown, for gene editing experimental validation of the high yield, and confirmed that OsSPL4 significantly affects panicle branching and increases the rice yield. This study, which included extensive field phenotyping, cross-tissue systems biology analyses, and functional validation, uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice. The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample. DCT can be downloaded from https://github.com/ztpub/DCT .